Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Cancer Prevention02:59

Cancer Prevention

Several factors can increase the risk of cancer in an individual. About 50% of cancer cases can be prevented by adopting a healthy lifestyle, regular exercise, eating healthy, and following a modest cancer prevention diet. Epidemiological studies have consistently shown that populations with vegetable and fruit-rich diets have reduced the incidence of cancer. On the other hand, populations who have a diet rich in animal fat, red meat, junk food, or high calories are predisposed to cancer.
Some...

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Use of liposomal irinotecan with 5-FU and oxaliplatin (NALIRIFOX) in neoadjuvant pancreatic adenocarcinoma: NEO-Nal-IRI trial.

The oncologist·2026
Same author

Assessing the quality of electronic health record data and the claims linked data for target trial emulation studies.

JAMIA open·2026
Same author

Agentic Authoring of OMOP Concept Sets from Natural Language.

medRxiv : the preprint server for health sciences·2026
Same author

Glucagon-Like Peptide-1 Receptor Agonists and Cardiovascular Events in Adults With Obesity and Autoimmune Disease: A Target Trial Emulation.

Journal of the American Heart Association·2026
Same author

Multimodal Prediction of Renal Tumor Malignancy From Radiology Reports and Structured Electronic Health Records: Retrospective Cohort Study.

JMIR medical informatics·2026
Same author

GatorDuo: Global-Consistency Dual-Graph Refinement With Pseudo-Label Agreement for Spatial Transcriptomics.

bioRxiv : the preprint server for biology·2026
Same journal

Real-World Impact of Electronic Patient-Reported Outcomes on Early Intervention Among Older Patients With Lung Cancer: Prospective Cohort Study.

JMIR cancer·2026
Same journal

Predictors of Telehealth Use Among Cancer Survivors: Retrospective Study.

JMIR cancer·2026
Same journal

Predicting Overall Survival in Patients With Multiple Primary Lung Cancer: Nomogram Development and Validation Study.

JMIR cancer·2026
Same journal

Large Language Models for Breast and Cervical Cancers Communication: Mixed Methods Evaluation Study Assessing Linguistic Quality, Safety, and Accessibility.

JMIR cancer·2026
Same journal

Multi-Turn LLM-Based Conversational Agents for Patients with Cancer and Caregivers: A Scoping Review.

JMIR cancer·2026
Same journal

Digital Intervention for Electronic Patient-Reported Outcomes in Advanced Cancer: Mixed Methods Study.

JMIR cancer·2026
查看所有相关文章

相关实验视频

Updated: Jun 7, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K

使用机器学习和真实世界的数据预测早期发病的结肠直肠癌在查年龄以下的个体:病例对照研究研究:机器学习和真实世界的数据.

Chengkun Sun1, Erin Mobley2,3, Michael Quillen4

  • 1Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, 1889 Museum Road, Office 7020, Gainesville, FL, 32611, United States, 1 3526279467.

JMIR cancer
|June 19, 2025
PubMed
概括
此摘要是机器生成的。

机器学习模型可以使用电子健康记录预测45岁以下人群的早期结直肠癌 (EOCRC). 这项研究确定了关键的风险因素,有助于早期诊断和年轻成年人的预防策略.

关键词:
这就是 SHAP SHAP 的意思.美国人 美国人 美国人在CRC中,CRC就是CRC.欧洲人权理事会 欧洲人权理事会ML ML 在 ML莎普利的添加式解释美国的美国.青少年 青少年 青少年 青少年结肠直肠癌是什么意思诊断 诊断 诊断 诊断 诊断 诊断电子健康记录 电子健康记录机器学习是机器学习.一个中年人,中年人.预测 预测 预测 预测预防和治疗 预防和治疗这是直肠癌.年轻人年轻人年轻人年轻人年轻人年轻人

更多相关视频

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

381
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

201

相关实验视频

Last Updated: Jun 7, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

381
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

201

科学领域:

  • 在瘤学瘤学.
  • 数据科学数据科学数据科学
  • 公共卫生 公共卫生

背景情况:

  • 结肠直肠癌是美国年轻人癌症死亡的主要原因.
  • 早期预测和了解早期结直肠癌 (EOCRC) 风险因素对于未达到推查年龄的患者至关重要.

研究的目的:

  • 使用机器学习 (ML) 和电子健康记录 (EHR) 数据,预测45岁以下个体的EOCRC.
  • 探索EOCRC早期诊断的潜在风险和保护因素.

主要方法:

  • 开发了用于结肠癌 (CC) 和直肠癌 (RC) 的单独ML模型,使用来自45岁以下患者的EHR数据.
  • 通过使用各种ML算法和倾向得分匹配,在多个时间窗口 (0-5年) 中评估预测.
  • 解释模型使用Shapley添加式解释来识别关键风险因素.

主要成果:

  • 模型的AUC得分高达0.811的CC和0.829的RC预测.
  • 关键的预测特征包括免疫/消化系统疾病,二次恶性瘤和体重不足状况;血液疾病是CC特有的.
  • 倾向性得分匹配确保了对混变量的稳定性.

结论:

  • 使用EHR数据的ML模型显示,在45岁以下的人群中,早期EOCRC预测具有潜在的EOCRC预测潜力.
  • 确定了重要的风险因素,可以为早期的检测和预防策略提供信息.
  • 这项研究提供了改善年轻人群体EOCRC结果的初步见解.